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dc.contributor.authorCañete Valverde, Ángel Jesús
dc.contributor.authorAmor-Pinilla, María Mercedes 
dc.contributor.authorFuentes, Lidia
dc.date.accessioned2021-10-08T09:11:31Z
dc.date.available2021-10-08T09:11:31Z
dc.date.issued2022-01
dc.identifier.citationAngel Cañete, Mercedes Amor, Lidia Fuentes, Supporting IoT applications deployment on edge-based infrastructures using multi-layer feature models, Journal of Systems and Software, Volume 183, 2022es_ES
dc.identifier.urihttps://hdl.handle.net/10630/22972
dc.description.abstractEdge Computing proposes to use the nearby devices in the frontier/Edge of the access network for deploying application tasks of IoT-based systems. However, the functionality of such cyber–physical systems, which is usually distributed in several devices and computers, imposes specific requirements on the infrastructure to run properly. The evolution of an application to meet new user requirements and the high diversity of hardware and software technologies in the IoT/Edge/Cloud can complicate the deployment of continuously evolving applications. The aim of our approach is to apply Multi Layer Feature Models, which capture the variability of applications and the software and hardware infrastructure, to support the deployment in edge-based environments of cyber–physical applications. With this multi-layered approach is possible to support the evolution of application and infrastructure independently. Considering that IoT/Edge/Cloud infrastructures are usually shared by many applications, the deployment process has to assure that there will be enough resources for all of them, informing developers about the feasible alternatives. We provide four modules so that the developer can calculate what is the configuration of minimal set of devices supporting application requirements of the evolved application. In addition, the developer can find what is the application configuration that can be hosted in the current infrastructure. The successive solutions of continuous deployment generated by our approach pursue the reduction of the system energy footprint and/or execution latency.es_ES
dc.description.sponsorshipThis work is supported by the European Union’s H2020 research and innovation programme under grant agreement DAEMON 101017109 and by the projects co-financed by FEDER funds, Spain LEIA UMA18-FEDERJA-15, MEDEA RTI2018-099213-B-I00 (MCI/AEI) and RHEA P18-FR-1081. Funding for open access charge: Universidad de Málaga/CBUA.es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectComputación en nubees_ES
dc.subjectInternet de las cosases_ES
dc.subject.otherTask allocation problemes_ES
dc.subject.otherEdge Computinges_ES
dc.subject.otherDevOpses_ES
dc.subject.otherMulti-layer feature modelses_ES
dc.subject.otherSMT Optimizationes_ES
dc.subject.otherSoftware Product Lineses_ES
dc.titleSupporting IoT applications deployment on edge-based infrastructures using multi-layer feature modelses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.centroE.T.S.I. Informáticaes_ES
dc.identifier.doihttps://doi.org/10.1016/j.jss.2021.111086
dc.rights.ccAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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